1,721,028 research outputs found
Contribution to the discussion of “Statistical challenges of administrative and transaction data”
No full textTransformed Variables in Survey Sampling
No full textIt can happen, especially in economic surveys, that we are interested in estimating the population mean or total of a variable Y, based on a sample, when a linear model seems appropriate, not for Y itself, but for a strictly monotone transformation of Y. In the present paper, we mainly focus on the important case where the transformation is logarithmic, but some new ideas introduced are not limited to that case. Currently available methods, based on the lognormal distribution, are reviewed, and two new methods introduced, one based on the idea of “smearing” (Duan, 1983), which do not require the lognormal assumption. Theoretical biases and variances are given, with suggestions for sample design and variance estimation, and a practical measure for reducing sensitivity to deviant points is suggested. We evaluate and compare the different estimators we describe in an extensive empirical study based on four economic populations taken from the UK Monthly Wages and Salaries Survey
Small area estimation of survey weighted counts under aggregated level spatial model
No full textThe empirical predictor under an area level version of the generalized linear mixed model (GLMM) is extensively used in small area estimation (SAE) for counts. However, this approach does not use the sampling weights or clustering information that are essential for valid inference given the informative samples produced by modern complex survey designs. This paper describes an SAE method that incorporates this sampling information when estimating small area proportions or counts under an area level version of the GLMM. The approach is further extended under a spatial dependent version of the GLMM (SGLMM). The mean squared error (MSE) estimation for this method is also discussed. This SAE method is then applied to estimate the extent of household poverty in different districts of the rural part of the state of Uttar Pradesh in India by linking data from the 2011-12 Household Consumer Expenditure Survey collected by the National Sample Survey Office (NSSO) of India, and the 2011 Indian Population Census. Results from this application indicate a substantial gain in precision for the new methods compared to the direct survey estimates
Modelling group heterogeneity for small area estimation using M-quantiles
No full textSmall area estimation typically requires model-based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M-quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.</p
Maximum Likelihood Under Response Biased Sampling
No full textInformative sampling occurs when the probability of inclusion in sample depends on the value of the survey response variable. Response or size biased sampling is a particular case of informative sampling where the inclusion probability is proportional to the value of this variable. In this paper we describe a general model for response biased sampling, which we call array sampling, and develop maximum likelihood and estimating equation theory appropriate to this situation. The Missing Information Principle (MIP) (Orchard and Woodbury, 1972) yields one (indirect) approach to likelihood based survey inference (Breckling et al 1994). Some have questioned its applicability in the case of informative sampling, because of the way it conditions on the given sample. In this paper we describe a direct approach and show that it and the MIP-based approach lead to identical results under array sampling. Comparison is made to the weighted likelihood based approach described in Krieger and Pfeffermann (1992). Extensions to the theory are also explored
Small area estimates for cross-classifications
No full textWe develop a class of log-linear structural models that is suited to estimation of small area cross-classified counts based on survey data. This allows us to account for various associ- ation structures within the data and includes as a special case the restricted log-linear model underlying structure preserving estimation. The effect of survey design can be incorporated into estimation through the specification of an unbiased direct estimator and its associated covariance structure. We illustrate our approach by applying it to estimation of small area labour force characteristics in Norway. Copyright 2004 Royal Statistical Society.
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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